OpenRaiser/Intern-Atlas huggingface dataset:
A Three-Dimensional GUI for Windows Explorer
Venue: arXiv cs.HC 2015
Year: 2015

Three-dimension will be a characteristic of future user interfaces, although we are just starting to gain an understanding of how users can navigate and share information within a virtual 3D environment. Three-dimensional graphical user interfaces (3D-GUI) raise many issues of design, metaphor and usability. This research is devoted to designing a 3D-GUI as a front-end tool for a file management system, in this case, for Microsoft Windows\c{opyright} Explorer; as well as evaluating the efficiency of a 3D application. The software design was implemented by extending the Half-Life 3D engine. This extension provides a directory traversal and basic file management functions, like cut, copy, paste, delete, and so on. This paper shows the design and implementation of a real-world application that contains an efficient 3D-GUI.
A Binary Data Stream Scripting Language
Venue: arXiv cs.PL 2015
Year: 2015

Any file is fundamentally a binary data stream. A practical solution was achieved to interpret binary data stream. A new scripting language named Data Format Scripting Language (DFSL) was developed to describe the physical layout of the data in a structural, more intelligible way. On the basis of the solution, a generic software application was implemented; it parses various binary data streams according to their respective DFSL scripts and generates human-readable result and XML document for data sharing. Our solution helps eliminate the error-prone low-level programming, especially in the hardware devices or network protocol development/debugging processes.
On the Efficiency of the Proportional Allocation Mechanism for Divisible Resources
Venue: arXiv cs.GT 2015
Year: 2015

We study the efficiency of the proportional allocation mechanism, that is widely used to allocate divisible resources. Each agent submits a bid for each divisible resource and receives a fraction proportional to her bids. We quantify the inefficiency of Nash equilibria by studying the Price of Anarchy (PoA) of the induced game under complete and incomplete information. When agents' valuations are concave, we show that the Bayesian Nash equilibria can be arbitrarily inefficient, in contrast to the well-known 4/3 bound for pure equilibria. Next, we upper bound the PoA over Bayesian equilibria by 2 when agents' valuations are subadditive, generalizing and strengthening previous bounds on lattice submodular valuations. Furthermore, we show that this bound is tight and cannot be improved by any simple or scale-free mechanism. Then we switch to settings with budget constraints, and we show an improved upper bound on the PoA over coarse-correlated equilibria. Finally, we prove that the PoA is exactly 2 for pure equilibria in the polyhedral environment.
Compensation of Nonlinear Torsion in Flexible Joint Robots: Comparison of Two Approaches
Venue: arXiv cs.SY 2015
Year: 2015

Flexible joint robots, in particularly those which are equipped with harmonic-drive gears, can feature elasticities with hysteresis. Under heavy loads and large joint torques the hysteresis lost motion can lead to significant errors of tracking and positioning of the robotic links. In this paper, two approaches for compensating the nonlinear joint torsion with hysteresis are described and compared with each other. Both methods assume the measured signals available only on the motor side of joint transmissions. The first approach assumes a rigid-link manipulator model and transforms the desired link trajectory into that of the motor drives by using the inverse dynamics and inverse hysteresis map. The second approach relies on the modeling of motor drives and inverse hysteresis and uses the generalized momenta when predicting the joint torsion. Both methods are discussed in details along with a numerical example of two-link planar manipulator under gravity.
Thinning Algorithm Using Hypergraph Based Morphological Operators
Venue: arXiv cs.CV 2015
Year: 2015

The object recognition is a complex problem in the image processing. Mathematical morphology is Shape oriented operations, that simplify image data, preserving their essential shape characteristics and eliminating irrelevancies. This paper briefly describes morphological operators using hypergraph and its applications for thinning algorithms. The morphological operators using hypergraph method is used to preventing errors and irregularities in skeleton, and is an important step recognizing line objects. The morphological operators using hypergraph such as dilation, erosion, opening, closing is a novel approach in image processing and it act as a filter remove the noise and errors in the images.
A Framework of Sparse Online Learning and Its Applications
Venue: arXiv cs.LG 2015
Year: 2015

The amount of data in our society has been exploding in the era of big data today. In this paper, we address several open challenges of big data stream classification, including high volume, high velocity, high dimensionality, high sparsity, and high class-imbalance. Many existing studies in data mining literature solve data stream classification tasks in a batch learning setting, which suffers from poor efficiency and scalability when dealing with big data. To overcome the limitations, this paper investigates an online learning framework for big data stream classification tasks. Unlike some existing online data stream classification techniques that are often based on first-order online learning, we propose a framework of Sparse Online Classification (SOC) for data stream classification, which includes some state-of-the-art first-order sparse online learning algorithms as special cases and allows us to derive a new effective second-order online learning algorithm for data stream classification. In addition, we also propose a new cost-sensitive sparse online learning algorithm by extending the framework with application to tackle online anomaly detection tasks where class distribution of data could be very imbalanced. We also analyze the theoretical bounds of the proposed method, and finally conduct an extensive set of experiments, in which encouraging results validate the efficacy of the proposed algorithms in comparison to a family of state-of-the-art techniques on a variety of data stream classification tasks.
An Optimal Resource Allocation with Frequency Reuse in Cellular Networks
Venue: arXiv cs.NI 2015
Year: 2015

In this paper, we introduce a novel approach for optimal resource allocation with frequency reuse for users with elastic and inelastic traffic in cellular networks. In our model, we represent users' applications running on different user equipments (UE)s by logarithmic and sigmoid utility functions. We applied utility proportional fairness allocation policy, i.e. the resources are allocated among users with fairness in utility percentage of the application running on each mobile station. Our objective is to allocate the cellular system resources to mobile users optimally from a multi-cell network. In our model, a minimum quality-of-service (QoS) is guaranteed to every user subscribing for the mobile service with priority given to users with real-time applications. We show that the novel resource allocation optimization problem with frequency reuse is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate the resources optimally from Mobility Management Entity (MME) to base stations (BS)s sectors. Finally, we present the simulation results for the performance of our rate allocation algorithm.
Combining Fixed-Point Definitions and Game Semantics in Logic Programming
Venue: arXiv cs.LO 2015
Year: 2015

Logic programming with fixed-point definitions is a useful extension of traditional logic programming. Fixed-point definitions can capture simple model checking problems and closed-world assumptions. Its operational semantics is typically based on intuitionistic provability. We extend the operational semantics of these languages with game semantics. This extended semantics has several interesting aspects: in particular, it gives a logical status to the $read$ predicate.
Estimator Selection: End-Performance Metric Aspects
Venue: arXiv cs.IT 2015
Year: 2015

Recently, a framework for application-oriented optimal experiment design has been introduced. In this context, the distance of the estimated system from the true one is measured in terms of a particular end-performance metric. This treatment leads to superior unknown system estimates to classical experiment designs based on usual pointwise functional distances of the estimated system from the true one. The separation of the system estimator from the experiment design is done within this new framework by choosing and fixing the estimation method to either a maximum likelihood (ML) approach or a Bayesian estimator such as the minimum mean square error (MMSE). Since the MMSE estimator delivers a system estimate with lower mean square error (MSE) than the ML estimator for finite-length experiments, it is usually considered the best choice in practice in signal processing and control applications. Within the application-oriented framework a related meaningful question is: Are there end-performance metrics for which the ML estimator outperforms the MMSE when the experiment is finite-length? In this paper, we affirmatively answer this question based on a simple linear Gaussian regression example.
Learning (Predictive) Risk Scores in the Presence of Censoring due to Interventions
Venue: arXiv cs.AI 2015
Year: 2015

A large and diverse set of measurements are regularly collected during a patient's hospital stay to monitor their health status. Tools for integrating these measurements into severity scores, that accurately track changes in illness severity, can improve clinicians ability to provide timely interventions. Existing approaches for creating such scores either 1) rely on experts to fully specify the severity score, or 2) train a predictive score, using supervised learning, by regressing against a surrogate marker of severity such as the presence of downstream adverse events. The first approach does not extend to diseases where an accurate score cannot be elicited from experts. The second approach often produces scores that suffer from bias due to treatment-related censoring (Paxton, 2013). We propose a novel ranking based framework for disease severity score learning (DSSL). DSSL exploits the following key observation: while it is challenging for experts to quantify the disease severity at any given time, it is often easy to compare the disease severity at two different times. Extending existing ranking algorithms, DSSL learns a function that maps a vector of patient's measurements to a scalar severity score such that the resulting score is temporally smooth and consistent with the expert's ranking of pairs of disease states. We apply DSSL to the problem of learning a sepsis severity score using a large, real-world dataset. The learned scores significantly outperform state-of-the-art clinical scores in ranking patient states by severity and in early detection of future adverse events. We also show that the learned disease severity trajectories are consistent with clinical expectations of disease evolution. Further, using simulated datasets, we show that DSSL exhibits better generalization performance to changes in treatment patterns compared to the above approaches.
Modularity for Security-Sensitive Workflows
Venue: arXiv cs.SE 2015
Year: 2015

An established trend in software engineering insists on using components (sometimes also called services or packages) to encapsulate a set of related functionalities or data. By defining interfaces specifying what functionalities they provide or use, components can be combined with others to form more complex components. In this way, IT systems can be designed by mostly re-using existing components and developing new ones to provide new functionalities. In this paper, we introduce a notion of component and a combination mechanism for an important class of software artifacts, called security-sensitive workflows. These are business processes in which execution constraints on the tasks are complemented with authorization constraints (e.g., Separation of Duty) and authorization policies (constraining which users can execute which tasks). We show how well-known workflow execution patterns can be simulated by our combination mechanism and how authorization constraints can also be imposed across components. Then, we demonstrate the usefulness of our notion of component by showing (i) the scalability of a technique for the synthesis of run-time monitors for security-sensitive workflows and (ii) the design of a plug-in for the re-use of workflows and related run-time monitors inside an editor for security-sensitive workflows.
Fast Segmentation of Left Ventricle in CT Images by Explicit Shape Regression using Random Pixel Difference Features
Venue: arXiv cs.CV 2015
Year: 2015

Recently, machine learning has been successfully applied to model-based left ventricle (LV) segmentation. The general framework involves two stages, which starts with LV localization and is followed by boundary delineation. Both are driven by supervised learning techniques. When compared to previous non-learning-based methods, several advantages have been shown, including full automation and improved accuracy. However, the speed is still slow, in the order of several seconds, for applications involving a large number of cases or case loads requiring real-time performance. In this paper, we propose a fast LV segmentation algorithm by joint localization and boundary delineation via training explicit shape regressor with random pixel difference features. Tested on 3D cardiac computed tomography (CT) image volumes, the average running time of the proposed algorithm is 1.2 milliseconds per case. On a dataset consisting of 139 CT volumes, a 5-fold cross validation shows the segmentation error is $1.21 \pm 0.11$ for LV endocardium and $1.23 \pm 0.11$ millimeters for epicardium. Compared with previous work, the proposed method is more stable (lower standard deviation) without significant compromise to the accuracy.
On the Simulation of Trusted Routing Mechanism
Venue: arXiv cs.NI 2015
Year: 2015

Among the mechanisms for the data security in computer networks is considered trusted routing. Its simulation method is chosen and choice of network simulator is substantiated.
Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds
Venue: arXiv cs.CL 2015
Year: 2015

Recent work has explored methods for learning continuous vector space word representations reflecting the underlying semantics of words. Simple vector space arithmetic using cosine distances has been shown to capture certain types of analogies, such as reasoning about plurals from singulars, past tense from present tense, etc. In this paper, we introduce a new approach to capture analogies in continuous word representations, based on modeling not just individual word vectors, but rather the subspaces spanned by groups of words. We exploit the property that the set of subspaces in n-dimensional Euclidean space form a curved manifold space called the Grassmannian, a quotient subgroup of the Lie group of rotations in n- dimensions. Based on this mathematical model, we develop a modified cosine distance model based on geodesic kernels that captures relation-specific distances across word categories. Our experiments on analogy tasks show that our approach performs significantly better than the previous approaches for the given task.
Interference-Cooperation in Multi-User/Multi-Operator Receivers
Venue: arXiv cs.IT 2015
Year: 2015

In a multi-user scenario where users belong to different operators, any interference mitigation method needs unavoidably some degree of cooperation among service providers. In this paper we propose a cooperation strategy based on the exchange of mutual interference among operators, rather than of decoded data, to let every operator to recover an augmented degree of diversity either for channel estimation and multi-user detection. In xDSL scenario where multiple operators share the same cable binder the interference-cooperation (IC) approach outperforms data-exchange methods and preserves to certain degree the privacy of the users as signals can be tailored to prevent each operator to infer parameters (channel and data) of the users from the other operators. The IC method is based on Expectation Maximization estimation shaped to account for the degree of information that each operator can exchange with the others during the two steps of multi-user channel estimation and multi-user detection. Convergence of IC is guaranteed into few iterations and it does not depend on the structure of the interference. IC performance attains those of centralized receivers (i.e., one fusion-center that collects all the received signals from all the users/operators), with some loss when in heavily interfered multi-user channel such as in twisted-pair communications allocated beyond 50-100MHz spectrum.
Scaling up Greedy Causal Search for Continuous Variables
Venue: arXiv cs.AI 2015
Year: 2015

As standardly implemented in R or the Tetrad program, causal search algorithms used most widely or effectively by scientists have severe dimensionality constraints that make them inappropriate for big data problems without sacrificing accuracy. However, implementation improvements are possible. We explore optimizations for the Greedy Equivalence Search that allow search on 50,000-variable problems in 13 minutes for sparse models with 1000 samples on a four-processor, 16G laptop computer. We finish a problem with 1000 samples on 1,000,000 variables in 18 hours for sparse models on a supercomputer node at the Pittsburgh Supercomputing Center with 40 processors and 384 G RAM. The same algorithm can be applied to discrete data, with a slower discrete score, though the discrete implementation currently does not scale as well in our experiments; we have managed to scale up to about 10,000 variables in sparse models with 1000 samples.
Duality and Nonlinear Graph Laplacians
Venue: arXiv cs.DS 2015
Year: 2015

We present an iterative algorithm for solving a class of \\nonlinear Laplacian system of equations in $\tilde{O}(k^2m \log(kn/ε))$ iterations, where $k$ is a measure of nonlinearity, $n$ is the number of variables, $m$ is the number of nonzero entries in the graph Laplacian $L$, $ε$ is the solution accuracy and $\tilde{O}()$ neglects (non-leading) logarithmic terms. This algorithm is a natural nonlinear extension of the one by of Kelner et. al., which solves a linear Laplacian system of equations in nearly linear time. Unlike the linear case, in the nonlinear case each iteration takes $\tilde{O}(n)$ time so the total running time is $\tilde{O}(k^2mn \log(kn/ε))$. For sparse graphs where $m = O(n)$ and fixed $k$ this nonlinear algorithm is $\tilde{O}(n^2 \log(n/ε))$ which is slightly faster than standard methods for solving linear equations, which require approximately $O(n^{2.38})$ time. Our analysis relies on the construction of a nonlinear "energy function" and a nonlinear extension of the duality analysis of Kelner et. al to the nonlinear case without any explicit references to spectral analysis or electrical flows. These new insights and results provide tools for more general extensions to spectral theory and nonlinear applications.
A Multi-Camera Image Processing and Visualization System for Train Safety Assessment
Venue: arXiv cs.CV 2015
Year: 2015

In this paper we present a machine vision system to efficiently monitor, analyze and present visual data acquired with a railway overhead gantry equipped with multiple cameras. This solution aims to improve the safety of daily life railway transportation in a two- fold manner: (1) by providing automatic algorithms that can process large imagery of trains (2) by helping train operators to keep attention on any possible malfunction. The system is designed with the latest cutting edge, high-rate visible and thermal cameras that ob- serve a train passing under an railway overhead gantry. The machine vision system is composed of three principal modules: (1) an automatic wagon identification system, recognizing the wagon ID according to the UIC classification of railway coaches; (2) a temperature monitoring system; (3) a system for the detection, localization and visualization of the pantograph of the train. These three machine vision modules process batch trains sequences and their resulting analysis are presented to an operator using a multitouch user interface. We detail all technical aspects of our multi-camera portal: the hardware requirements, the software developed to deal with the high-frame rate cameras and ensure reliable acquisition, the algorithms proposed to solve each computer vision task, and the multitouch interaction and visualization interface. We evaluate each component of our system on a dataset recorded in an ad-hoc railway test-bed, showing the potential of our proposed portal for train safety assessment.
Comparison between security majors in virtual machine and linux containers
Venue: arXiv cs.CR 2015
Year: 2015

Virtualization started to gain traction in the domain of information technology in the early 2000s when managing resource distribution was becoming an uphill task for developers. As a result, tools like VMWare, Hyper V (hypervisor) started making inroads into the software repository on different operating systems. VMWare and Hyper V could support multiple virtual machines running on them with each having their own isolated environment. Due to this isolation, the security aspects of virtual machines (VMs) did not differ much from that of physical machines (having a dedicated operating system on hardware). The advancement made in the domain of linux containers (LXC) has taken virtualization to an altogether different level where resource utilization by various applications has been further optimized. But the container security has assumed primary importance amongst the researchers today and this paper is inclined towards providing a brief overview about comparisons between security of container and VMs.
Pseudo-Cores: The Terminus of an Intelligent Viral Meme's Trajectory
Venue: arXiv cs.SI 2015
Year: 2015

Comprehending the virality of a meme can help us in addressing the problems pertaining to disciplines like epidemiology and digital marketing. Therefore, it is not surprising that memetics remains a highly analyzed research topic ever since the mid 1990s. Some scientists choose to investigate the intrinsic contagiousness of a meme while others study the problem from a network theory perspective. In this paper, we revisit the idea of a core-periphery structure and apply it to understand the trajectory of a viral meme in a social network. We have proposed shell-based hill climbing algorithms to determine the path from a periphery shell(where the meme originates) to the core of the network. Further simulations and analysis on the networks behavioral characteristics helped us unearth specialized shells which we term Pseudo-Cores. These shells emulate the behavior of the core in terms of size of the cascade triggered. In our experiments, we have considered two sets for the target nodes, one being core and the other being any of the pseudo-core. We compare our algorithms against already existing path finding algorithms and validate the better performance experimentally.
A quantum extension to inspection game
Venue: arXiv cs.GT 2015
Year: 2015

Quantum game theory is a new interdisciplinary field between game theory and physical research. In this paper, we extend the classical inspection game into a quantum game version by quantizing the strategy space and importing entanglement between players. Our result shows that the quantum inspection game has various Nash equilibrium depending on the initial quantum state of the game. It is also shown that quantization can respectively help each player to increase his own payoff, yet fails to bring Pareto improvement for the collective payoff in the quantum inspection game.